MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

Segmentation of retinal structures, namely optic disc, vessel, demarcation line, and ridge, is essential for describing the characteristics of Retinopathy of Prematurity (ROP). Computerized systems are being developed for automatic segmentation in fundus images to assist the medical experts and bring consistency in the diagnosis. There are multiple challenges in the segmentation task of premature infants’ fundus images. The annotation and ground truth preparation required for the segmentation is complex, challenging, and expensive. Further, ROP datasets are not available publicly, and hence carrying out such a task needs a primary dataset and significant assistance from the domain expert. To address this gap, two primary datasets named HVDROPDB-BV and HVDROPDB-RIDGE were developed. The datasets consist of images captured by two different imaging systems, having different sizes, resolutions, and illumination. This made the trained models generic and robust to data variability and heterogeneity. We propose the modified U-Net architectures by incorporating squeeze and excitation (SE) blocks and attention gates (AG) to segment the demarcation line/ridge and vessel from these datasets. These modifications were tested and validated by ROP experts. The performance of all the three networks (U-Net, AG U-Net, and SE U-Net) was promising, with a variation of 1 to 6% in the dice coefficient for the HVDROPDB datasets. The area under the curve (AUC) obtained for all three networks was above 0.94, indicating them as excellent models. AG U-Net outperformed the other two networks, with a sensitivity of 96% and specificity of 89% for stage detection via new test images. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

School of Computer Engineering & Technology

Engineering and Technology

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Multimedia Tools and Applications

Deep dive in retinal fundus image segmentation using deep learning for retinopathy of prematurity

Retinopathy of prematurity (ROP) is a potentially blinding disorder seen in low birth weight preterm infants. In India, the burden of ROP is high, with nearly 200,000 premature infants at risk. Early detection through screening and treatment can prevent this blindness. The automatic screening systems developed so far can detect "severe ROP" or "plus disease," but this information does not help schedule follow-up. Identifying vascularized retinal zones and detecting the ROP stage is essential for follow-up or discharge from screening. There is no automatic system to assist these crucial decisions to the best of the authors' knowledge. The low contrast of images, incompletely developed vessels, macular structure, and lack of public data sets are a few challenges in creating such a system. In this paper, a novel method using an ensemble of "U-Network" and "Circle Hough Transform" is developed to detect zones I, II, and III from retinal images in which macula is not developed. The model developed is generic and trained on mixed images of different sizes. It detects zones in images of variable sizes captured by two different imaging systems with an accuracy of 98%. All images of the test set (including the low-quality images) are considered. The time taken for training was only 14 min, and a single image was tested in 30 ms. The present study can help medical experts interpret retinal vascular status correctly and reduce subjective variation in diagnosis.Keywords: Artificial Intelligence; Automatic zone detection; Machine learning; Retinopathy of prematurity(ROP); Segmentation; U-Net.

Journal of Digital Imaging

Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning

Retinal vessel segmentation aims to separate the structure of blood vessels from the fundus image background. This retinal vasculature is then used for detection of numerous diseases like retinopathy of prematurity (ROP), glaucoma, diabetic retinopathy, coronary heart disease, etc. Deep learning-based semantic segmentation techniques are considered a breakthrough in the field of medical diagnosis using artificial intelligence. Various methods for segmentation with convolutional neural networks have been developed which have become indispensable in tackling more advanced challenges with image segmentation. The limitation is that they all require huge quantities of labeled data which is difficult to collect. To overcome this, U-Net architecture is widely used for segmentation of medical images as it segments the pixels individually and can be trained with a small number of images. In this work, we have implemented U-Net architecture and evaluated it on two public datasets: ‘HRF’ and ‘DRIVE’. An accuracy of 96.64% was obtained on the DRIVE dataset and 94.28% on HRF dataset. To check the model robustness, we tested the model trained on the augmented DRIVE dataset on the HRF dataset and vice versa. The model trained on the augmented HRF training set achieves an accuracy of 95.04% when tested on the DRIVE dataset. Similarly, the model trained on the augmented DRIVE training set achieves an accuracy of 92.17% when tested on the HRF dataset. A progressive web application is also developed as part of this work, since there is no specific easy-to-use interface to perform segmentation of retinal fundus mentioned in the literature, to the best of our knowledge. This application accepts retinal fundus image as input and performs segmentation using trained U-Net model, to provide an output image of the blood vessels which will assist the ophthalmologists in screening retinopathy.<h2>Keywords</h2>U-Net&nbsp;Deep learning&nbsp;Semantic segmentation&nbsp;Retinal image segmentation&nbsp;Blood vessel patterns&nbsp;Machine learning&nbsp;

Intelligent Learning for Computer Vision

Deep Learning-Based Framework for Retinal Vasculature Segmentation

Retinopathy of Prematurity(ROP) is a disease of the eyeand a potential sourceof blindness in low birth weight preterm infants. It is preventable if diagnosed and treated on time. Artificial Intelligence (AI) has played an important role in developing automated screening systems to assist medical experts. There are many traditional literature review articles available that focus on the scientific content of ROP-AI. The researchers also require a bibliometric analysis to become acquainted with the competing groups and new trends in this field. This paper gives a brief overview of ROP and AI systems for ROP screening with a statistical analysis of the Scopus database's related documents.Keywords:retinopathy of prematurity, deep learning, machine learning, automated, artificial intelligence,bibliometric survey

•	Library Philosophy and Practice

Quantitative analysis of research on Artificial Intelligence in Retinopathy of Prematurity

International Journal for Scientific Research & Development|

Conflict Solving with the Use of Driver Distraction

Vehicle technologies have advanced significantly over the past 20 years, especially with respect to novel in-vehicle systems for route navigation, information access, infotainment, and connected vehicle advancements for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) connectivity and communications. While there is great interest in migrating to fully automated, self-driving vehicles, factors such as technology performance, cost barriers, public safety, insurance issues, legal implications, and government regulations suggest it is more likely that the first step in the progression will be multifunctional vehicles.Today, embedded controllers, as well as a variety of sensors and high-performance computing in present-day cars, allow for a smooth transition from complete human control toward semisupervised or assisted control, then to fully automated vehicles. Next-generation vehicles will need to be more active in assessing driver awareness, vehicle capabilities, and traffic and environmental settings, plus how these factors come together to determine a collaborative safe and effective driver–vehicle engagement for vehicle operation.This article reviews a range of issues pertaining to driver modeling for the detection and assessment of distraction. The areas addressed include 1) understanding driver behavior and distraction, 2) maneuver recognition and distraction analysis, 3) glance behavior and visual tracking, and 4) mobile platform advancements for in-vehicle data collection and human-machine interface 5) Alcohol Detection

International Research Journal of Engineering and Technology

Survey : Automatic Understanding By Vehicle For Driver Distraction Problem

Distraction is nothing but the lacking of the attention to activities necessary for secure driving. Inattention can either be an intended or an unintended diversion of concentration from drivers side. Driver distraction can be defined as anything that is necessary for identification of information to securely sustain the lateral and longitudinal control of the vehicle due to any some event, the persons, inside or outside the vehicle that might be forced or tends cause the drivers shifting attention to another place from the fundamental driving task. The main cause of driver distraction is to compete for event activity that turns to reduce driving performance that outcome in road traffic crashes. The system defined in this paper given solution to the above problem and can reduce the road accident which is one of the main causes due to driver distraction. It also evaluates the driver's mind become active or not by accurately detecting yawning, face detection, eye detection and to find whether the driver consumed an alcoholic or not. To attain an alcohol detection through the sensor. It is done for the safety of people traveling by car. The Haar cascade algorithm can be used for object detection in real time. © 2018 IEEE.

Proceedings - 2018 4th International Conference on Computing, Communication Control and Automation, ICCUBEA 2018

Camera Based Driver Distraction System Using Image Processing

Internet has become an indispensible part of today's life. World Wide Web (WWW) is the largest shared information source. Finding relevant information on the WWW is challenging. To respond to a user query, it is difficult to search through the large number of returned documents with the presence of today's search engines. There is a need to organize a large set of documents into categories through clustering. The documents can be a user query or simply a collection of documents. Document clustering is the task of combining a set of documents into clusters so that intra cluster documents are similar to each other than inter cluster documents. Partitioning and Hierarchical algorithms are commonly used for document clustering. Existing partitioning algorithms have the limitation that the number of clusters has to be given as input and the clustering result depends on this input. If the number of clusters is not known, results are not acceptable. In this paper, we have developed a novel algorithm which generates number of clusters automatically for any unknown text dataset and clusters the documents appropriately based on Cosine Similarity between them. We have also detected zero clustering issue in partitioning algorithm and solved it using our novel algorithm. © 2013 IEEE.

Proceedings of the 2013 3rd IEEE International Advance Computing Conference, IACC 2013

A novel algorithm for automatic document clustering

Document clustering is the task of grouping a set of documents into clusters so that the documents in the same cluster are similar to each other than to those in other clusters. One of the applications of document clustering is in web search engine retrieval system to help the users find relevant information quicker, and allow them to focus their search in the appropriate direction. K-means is a commonly used algorithm for document clustering, but it has some disadvantages. The main limitations of K-means are: 1) The number of clusters K has to be given as input and 2) Based on the initializations it converges to different local minima. 3) It is slow and cannot be used for large number of data points.4) It cannot handle empty clusters. In this paper, we have developed a novel algorithm to eliminate all these basic drawbacks of K-means.